logic puzzles
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Benchmarking LLMs on Nonogram Solving
Read Full Article: Benchmarking LLMs on Nonogram Solving
A benchmark was developed to assess the ability of 23 large language models (LLMs) to solve nonograms, which are grid-based logic puzzles. The evaluation revealed that performance significantly declines as the puzzle size increases from 5×5 to 15×15. Some models resort to generating code for brute-force solutions, while others demonstrate a more human-like reasoning approach by solving puzzles step-by-step. Notably, GPT-5.2 leads the performance leaderboard, and the entire benchmark is open source, allowing for future testing as new models are released. Understanding how LLMs approach problem-solving in logic puzzles can provide insights into their reasoning capabilities and potential applications.
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